Channel integrity detection

ABSTRACT

A computer-implemented method can include determining an amplitude for each of a plurality of input channels, corresponding to respective nodes. A measure of similarity can be computed between the input channel of each node and the input channel of its neighboring nodes. The method can also include comparing an amplitude for each node relative to other nodes to determine temporary bad channels. For each of the temporary bad channels, a measure of similarity can be computed between the input channel of each node and the input channel of its neighboring nodes. Channel integrity can then be identified based on the computed measures of similarity.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.13/890,058 filed May 8, 2013, and entitled CHANNEL INTEGRITY DETECTIONwhich claims the benefit of U.S. Provisional Patent Application No.61/644,746, filed May 9, 2012 and entitled Automatic Bad ChannelDetection. Each of the above-identified applications is incorporatedherein by reference in its entirety.

TECHNICAL FIELD

This disclosure relates to channel integrity detection.

BACKGROUND

In some examples, body surface electrical activity (e.g., ECG signals)can be sensed by an arrangement of electrodes. The sensed signals can beprocessed for a variety of applications, such as for body surfacemapping or electrocardiographic mapping. Since these and otherprocessing methods can depend on body surface potential data, thequality of data for each input channel can affect the quality of theoutput results based on signal processing. In some types of signalprocessing, the signal processing can be very sensitive to anomalies inthe input channels. For instance, significant noise, such as line noiseor large changes in amplitude, or other variations in the input channelscould produce inaccurate results as well as overshadow the importantphysiological information. This could render the resulting outputscomputed from such input channels non-diagnostic or uninterpretable.

SUMMARY

This disclosure relates to channel integrity detection, such as tomitigate undesirable effects of noisy input channels on furtherprocessing and analysis.

In one example, the channel integrity detection can be implemented as anon-transitory computer readable medium having instructions. Theinstructions can include a preprocessing stage to analyze input channeldata for a plurality of input channels to detect channels having anintegrity that is considered one of bad or good, each of the pluralityof input channels corresponding to a respective one of a plurality ofnodes. A first spatial similarity measurement function can compute ameasure of similarity between the input channel data for each of theplurality of nodes and a set of neighboring nodes to identify a spatialcorrelated set of channels having an integrity that is considered one ofbad or good. An amplitude analyzer function can determine a subset ofchannels meeting amplitude criteria. A second spatial similaritymeasurement function can compute, for each channel in the subset ofchannels meeting the amplitude criteria, a measure of similarity betweenthe input channel data for each node and a set of neighboring nodes toidentify an amplitude correlated set of channels having an integritythat is considered one of bad or good. A combiner can store output datarepresenting the integrity of plurality of input channels based on thechannels detected by the preprocessing stage, the channels identified bythe spatial correlated set of channels and the channels identified bythe amplitude correlated set of channels.

In another example, a computer-implemented method can includedetermining an amplitude for each of a plurality of input channels,corresponding to respective nodes. A measure of similarity can becomputed between the input channel of each node and the input channel ofits neighboring nodes. The method can also include comparing anamplitude for each node relative to other nodes to determine temporarybad channels. For each of the temporary bad channels, a measure ofsimilarity can be computed between the input channel of each node andthe input channel of its neighboring nodes. Channel integrity can thenbe identified based on the computed measures of similarity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a channel integrity detection system.

FIG. 2 depicts another example of a channel integrity detection system.

FIG. 3 depicts an example of an electrophysiological mapping system thatcan implement channel integrity detection.

FIG. 4 depicts an example of a graphical user interface demonstratingexamples of high amplitude signals that can be identified via channelintegrity detection.

FIG. 5 depicts an example of another graphical user interfacedemonstrating examples of low spatially correlated signals that can beidentified via channel integrity detection.

FIG. 6 depicts a graphical representation of sensing nodes that can bedistributed across a patient's body surface.

FIG. 7 depicts an enlarged view of a part of the nodes of FIG. 6demonstrating a mesh configuration.

FIG. 8A depicts a representation of a node mesh structure demonstratinga central node surrounded by a set of local neighboring nodes.

FIG. 8B is an enlarged view of part of the mesh structure of FIG. 8Afurther demonstrating the central node and its local neighboring nodes.

FIG. 9 depicts an example of a graphical user interface demonstratingadditional signals that have been selected, a display of channelintegrity as well as an example map that can be generated based on thesignals detected by the sensing nodes.

FIG. 10 depicts another example of a graphical user interfacedemonstrating examples of channel signals, sensing node integrity and aresulting map that can be generated based on the input signals detectedby the sensing nodes.

DETAILED DESCRIPTION

This disclosure relates to an apparatus, system or method that candetermine channel integrity for a plurality of input channels. Each ofthe input channels can carry sensed electrical signals, such aselectrophysiological signals from a patient. The sensed electricalsignals for the respective channels can provide input channel data. Insome examples, the approach disclosed herein can detect channels thatmay be detrimental to further signal processing sensitive to anomaloussignals, such as line noise, large changes in amplitude or othervariations in the input channels. The channel integrity detectiondisclosed herein thus enables detection and removal of channelsdetermined to adversely affect such computations. In some examples, thedetection and removal can be fully automated or semi-automated.

The channel integrity detection can perform pre-processing on the inputchannel data to identify certain types of faults or invalid channels,such as can include detecting disconnected sensing electrodes (e.g.,saturated input channels), or other amplitude ranges (e.g., low and highamplitude ranges) that might adversely affect the signal processing.Additionally processing can be performed to compute a measure of spatialsimilarity (e.g., correlation) between the signals for a given node andits respective neighboring nodes. Signal channels having a low spatialcorrelation or otherwise uncorrelated relative to their respectiveneighbors can be identified as low integrity channels (e.g., alsoreferred to herein as “bad channels”), and thus can be removed fromfurther signal processing and analysis. Additional amplitude analysiscan be performed for additional channel integrity detection. Theamplitude analysis can be performed to identify outlier channels meetingcertain amplitude conditions, on which additional similaritymeasurements can be performed to identify a further subset of channelsthat may have low channel integrity. Each of the identified lowintegrity channels, based on the preprocessing, the spatial similaritymeasurement and the amplitude analysis, can be combined to create a listof bad channels. The identified bad channels can be removed from furtherprocessing and signal analysis, such as to provide input channel datathat includes the higher integrity channels.

As an example, the further processing and analysis can includereconstructing signals on a body surface based upon the input channeldata (e.g., via an inverse solution). Additional calculations can beperformed on the reconstructed data, such as to generate one or moregraphical maps and characterize the reconstructed data. By removing suchoutlier channels from further processing, the approach can not onlyachieve improved accuracy in such further processing and analysis butalso improves the system's workflow, such as by reducing preprocessingtime.

Additionally, in some examples where a significant portion of thechannels have been identified as “bad channels”, a graphical map can begenerated to identify the area of low resolution on a surface structureso that a user can determine if the affected area resides within aregion of interest. A user can in turn select to continue in view of theidentified low resolution area or make additional adjustments withrespect to the sensing nodes that have been identified as “badchannels”. A graphical user interface can also be provided to allow auser to selectively include or exclude one or more input channels fromthe analysis such as may be used to manually override the automaticremoval of the identified bad channels.

FIG. 1 depicts an example of a channel integrity detection system 10that can be utilized to provide an indication of channel integrity for aplurality of input channels. The channel integrity system 10 can beimplemented as hardware, software (e.g., a non-transitory medium havingmachine readable instructions) or a combination of hardware andsoftware. Signal information associated with each of the plurality ofinput channels can be provided by input channel data 12. The inputchannel data 12 can correspond to a digital representation of the sensedanalog signals, such as electrophysiology information. In some examples,the input channel data 12 can be provided by sensing electrodes that areplaced on a body surface of the patient, which can be an internal bodysurface (e.g., invasive) or an external body surface (e.g.,non-invasive) or a combination thereof.

By way of example, the input channel data 12 can represent signalsacquired (e.g., in real time or previously) from a plurality of bodysurface electrodes that are distributed across a patient's body, such asthe thorax. The electrodes can be distributed evenly across the entirethorax, for example. In other examples, the electrodes can bedistributed across a selected surface area (e.g., a sensing zone), suchas corresponding to electrodes that are configured to detect electricalsignals corresponding to a predetermined region of interest. In someexamples, the input channel data 12 can correspond to filtered inputdata, such as based on line filtering and other signal processing (e.g.,offset correction, analog-to-digital conversion and the like) to removeselected noise components from the input signals of the respectivechannels.

The channel integrity detection system 10 can include preprocessing 14,such as can include one or more method or function programmed to analyzethe input channel data to identify certain types of outlier channels. Insome examples, the preprocessing 14 can involve analysis of each channelwithout consideration of its neighboring channels. As used herein, theconcept of neighbors, such a when referring to neighboring channels ornodes, refers to the spatial proximity of sensing electrodes or nodesthat detect the input signals used to provide the input channel data 12.Thus, the preprocessing 14 can relate to analysis of the input channeldata for each channel by itself.

As a further example, the preprocessing 14 can include detectingdisconnected channels, such as based on detecting the voltage or currenton the respective channels that can identify the channel and its sensoras being disconnected or non-operational. The preprocessing 14 can alsoinclude detecting low amplitude signals that may have an amplitude belowa predetermined low voltage threshold. The preprocessing 14 can alsoinclude evaluation of high amplitude signals, such as within apredetermined range or exceeding a high amplitude threshold. Each of theranges and user threshold associated with the preprocessing cancorrespond to default values or can be user programmable, such as inresponse to a user input.

A similarity measurement function 16 can be programmed to compute ameasure of similarity between input signals, based on the input channeldata 12, for each of the plurality nodes relative to a set of its localneighboring nodes. Each node's neighbors nodes can be determined fromnode geometry information, demonstrated in this example as node distance22. For example, the set of neighboring nodes for a given node caninclude a first adjacent set of neighboring nodes surrounding the givennode. The similarity measurement function 16 can thus identify channelintegrity for a spatially correlated set of channels. The set ofchannels and their integrity can correspond to good channels or badchannels or otherwise provide an identifier to distinguish between goodand bad channels based on the spatial similarity measurement. In someexamples, the similarity measurement function can determine if anychannels are low correlated or uncorrelated channels and, based on suchdetermination, identify a set of low integrity channels.

An amplitude analyzer 18 can evaluate the amplitude of each of therespective channels. Like the similarity measurement function 16, theamplitude analyzer 18 can be performed on a set of channels excludingthose that have been identified as low integrity channels by thepreprocessing 14. The amplitude analyzer 18 can determine a subset ofhigher amplitude outlier channels based on a comparison of channelamplitudes for at least a substantial portion of the other nodes. Forexample, the amplitude analyzer 18 can determine which node or nodes (ifany) have an amplitude greater than a statistically significantamplitude value derived from evaluation of amplitudes for all relevantchannels (e.g., one or more standard deviations from the meanamplitude). The resulting subset of statistically high amplitudechannels identified by the analyzer 18 thus can be further processed bysimilarity measurement function 20 to compute a measure of similarity(e.g., a correlation) between the input channel data 12 for each node ofthe subset and its local neighboring nodes. Since if the high amplitudechannels might be determined to be good channels if they correlate wellwith the other neighboring channels, they can be considered temporarybad channels in this analysis. The similarity measurement function 20can identify which statistically high amplitude channels exhibit a lowcorrelation relative to its neighbors and thus can be considered badchannels. Alternatively or additionally, the similarity measurementfunction 20 can identify which channels are high integrity channels.

In some examples, the amplitude analyzer 18 and the similaritymeasurement functions 16 and 20 can employ node distance 22 to determineneighboring nodes for each of the nodes being analyzed. Additionally,the inter-node distance can be used to further constrain the similaritymeasurement functions 16 and 20. For example, if the node distanceexceeds a predetermined distance, which can be a fixed value or be userprogrammable, such node can be excluded from analysis as neighboringnode even if it is an actual spatial neighboring node. That is, the nodedistance 22 can constrain the measure of similarities to a spatialsignificant set of one or more nodes for each node that is processed bythe amplitude analyzer 18 and the similarity measurement functions 16and 20.

The channel integrity detection system 10 can provide output channeldata 24 to identify a set of one or more nodes having low integrity suchthat it should be excluded from subsequent analysis. The output channeldata 24 can be provided in terms of a list of nodes indexed according toinput channel that can be provided to subsequent processing blocks sothat the corresponding data for a given channel is not utilized insubsequent signal processing and data analysis. As disclosed herein, theoutput channel data can be provided in terms of channel integrity thatis considered bad, good, or can identify both bad and good channels. Insome examples, a logic value (e.g., 0 or 1) can be used to specify if achannel is good or bad. In other examples, an integrity value can becalculated to provide range of values representing the integrity of eachchannel, such that the degree of goodness or badness can becharacterized by the output channel data. In an ideal situation, therewould be no bad channels and the input channel data 12 for all channelswould be utilized for further processing and analysis. In practice,however, the channel integrity detection system 10 can identify lowintegrity channels that can be removed from further processing andanalysis as to improve the results.

FIG. 2 depicts an example of a channel integrity detection system 50. Inthe example of FIG. 2, the channel integrity detection system 50 isdemonstrated in the context of body surface electrical measurements thatare represented by body surface electrical data 52 acquired for arespective patient over one or more time intervals. The body surfaceelectrical data 52, for example, can include measured electrical signals(e.g., surface potentials) obtained from a plurality of sensingelectrodes distributed across the body surface of a patient. Similar toother examples disclosed herein, the distribution of electrodes cancover substantially the entire thorax of a patient or the sensingelectrodes can be distributed across a predetermined section of the bodysurface such as configured for detecting electrical signalspredetermined as being sufficient to detect electrical informationcorresponding to a predetermined region of interest for the patient'sbody. In other For example, a set of electrodes can be preconfigured tocover a selected region of the patient's torso for monitoring atrialelectrical activity of one or both atrium of a patient's heart, such asfor studying atrial fibrillation. In other examples other preconfiguredsets of electrodes can be utilized according to applicationrequirements, which can include invasive and non-invasive measurements.

The body surface electrical data 52 can be stored in memory of acomputer. The body surface electrical data can represent real timeinformation that is streaming in from sensing electrodes as data isacquired from a patient's body or it can be stored from a previousstudy. Regardless of the temporal nature of the electrical data 52, thechannel integrity detection system can improve accuracy of its furtherprocessing and analysis. Additionally, while the example of FIG. 2 isdescribed in the context of channel detection for body surfaceelectrical data, it is to be understood that the channel integritydetection, as disclosed herein, is equally applicable to other types ofelectrical signals including other types of electrophysiological signals(e.g., electromyography, electroencephalography, electrooculography,audiology and the like) as well as non-physiological electrical signalsthat may be monitored in a variety of other contexts.

An initial channel constraint 54 can be applied to the body surfaceelectrical data 52. The channel constraint 54, for example can providean index map that can be applied to the body surface electrical data toidentify and remove channels that have been determined to be missing.For example, one or more electrodes can be physically removed from thesensing vest such that the information obtained by the channel is notrelevant to the subsequent processing and analysis.

In another example where the body surface electrical data is to bemapped via inverse reconstruction to an anatomic envelope different fromwhere the sensing has occurred, node geometry data 56 can be acquiredfor the sensing nodes. The node geometry data, for example, can identifythe location of the sensing nodes (corresponding to sensing electrodes)in a respective correlated system. For example the node geometry data 56can include a list of nodes, and neighbors for each node, such as can beproduced by segmenting imaging data that has been acquired by anappropriate imaging modality. Examples of imaging modalities includeultrasound, computed tomography (CT), 3D Rotational angiography (3DRA),magnetic resonance imaging (MRI), x-ray, positron emission tomography(PET), fluoroscopy, and the like. Such imaging can be performedseparately (e.g., before or after the measurements) utilized to generatethe electrical data 52. Alternatively, imaging may be performedconcurrently with recording the electrical activity that is utilized togenerate the patient electrical data 14. The node geometry data 56 canalso include coordinates (e.g., in three-dimensional space) for each ofthe nodes. Distances can be computed for neighboring nodes based on thecoordinates (e.g., according to a distance metric, such as Euclideandistance). This can be stored in the node geometry data or it can becomputed from such information by the system 50. In other examples, thenode geometry data 56 can be acquired by manual measurements betweensensing nodes or other means (e.g., a digitizer).

The channel constraint 54 thus can be programmed to identify a givenchannel corresponding to a node that was not appropriately segmented(e.g., no location in 3-D space exists for the node). Thus missingchannels and/or unsegmented channels can be flagged or otherwise removedfrom the body surface electrical data 52. The channel constrained datacan then be provided by the channel constraint function 54 for furtheranalysis.

A disconnected channel detector 58 thus can operate on the constrainedbody surface electrical data (from channel constraint function 54) todetermine if any channels have been disconnected from the substrate,such as the patient's body from which the measurements have beenacquired. As an example, the disconnected channel detector 58 can beconfigured to detect saturation of an input channel such as bymonitoring the value of the electrical signal. If the value of theelectrical signal for a channel exceeds a threshold value (e.g., about +or −500 mV) or has a predetermined value (e.g., 0 V) for a plurality ofconsecutive samples, the corresponding channel can be determined to bedisconnected. As an example, a measurement system (e.g., measurementsystem 110 of FIG. 3) to which the input channel signals are providedcan be configured to saturate and obtain a predetermined value (e.g.,about + or −500 mV) for a given channel if it loses contact with thebody surface. In this way, the disconnected channel detector 58 candetermine a saturated or disconnected channel which will be removed fromfurther processing.

A de-trend filter 60 can be applied to the remaining body surfaceelectrical data 52 (e.g., excluding bad channels that have beenidentified by the channel constraint 54 or the detector 58). Thede-trend filter 60, for example, can be configured to remove the meanvalue or linear trend from each input channel (e.g., by FFT processing),which can remove baseline drift or other trending offsets from eachrespective channel. Such de-trending facilitates subsequent processing,including calculation of amplitude values for every signal channel.Additionally, by applying the de-trend filter 60 on the data provided bythe disconnected channel detector 58 instead of before operation of thedisconnected channel detector, the detection of saturated anddisconnected channels is facilitated.

An amplitude calculator 62 is configured to compute a peak-to-peakamplitude on the de-trended input channel data for each of the channels.In the example where the body surface electrical data corresponds toelectrocardiographic (ECG) data, the amplitude can be computed onde-trended ECG data. The computed amplitude values can be stored inmemory with the body surface electrical data 52 associated with each ofthe channels. For example, the data 52 can be populated with anamplitude field according to the channel index with which the data isstored in memory.

A low amplitude detection function 64 can be programmed to determine ifthe calculated amplitude for each respective channel is below apredetermined low amplitude threshold. Each channel identified as a badchannel already (e.g., by channel constraint 54 and detector 58) can beexcluded from the low amplitude detection function 64. For example thepeak-to-peak amplitude of the signal for a given channel is less thanthe low voltage threshold, the given channels can be considered to be anextreme low amplitude and can be removed from further analysis (e.g., abad or low integrity channel). The low amplitude threshold can beprogrammable or it can be set to a predetermined default value (e.g.,about 1×10⁻⁸ mV).

A high amplitude detection function 66 can be programmed to detectchannels having an amplitude that is greater than a typical body surfaceelectrical signal (e.g., greater than a typical ECG signal). The highamplitude detection function 66 thus can be programmed to compare theamplitude calculated (e.g., by amplitude calculator 62) for each channelrelative to a high amplitude threshold. If the peak-to-peak amplitude ofa given channel exceeds the high amplitude threshold, the channel can beidentified in the electrical data 52 as a bad channel. The amplitudethreshold can be programmable in response to user input or it can be setto a default value (e.g., about greater than 10 mV). The detection canbe applied to each of the channels and the results stored in memory suchas part of the body surface electrical data.

The system 50 can also include a node distance analyzer 68 that isprogrammed to quantify or characterize relative distance between thesensing nodes that are distributed across the body surface. For example,it has been determined that if the distance between neighboring nodesexceeds a certain distance, a comparison between neighboring channelsmay no longer be valid. As a result, the node distance analyzer 68 canprogrammed to determine if the distance between neighboring nodesexceeds distance threshold. The distance threshold can be a defaultvalue or it can be programmable to a desired value in response to a userinput. The node distance analyzer 68 can analyze the nodes based on thenode geometry data 56. As mentioned above, the node geometry data 56 canbe obtained by a segmentation process performed on imaging data or othermeans.

The node distance analyzer 68 thus can be used to constrain spatiallycomparative processing, as disclosed herein, to include only thosesensors and its neighbors that are within a prescribed proximity of eachother. As a result, the likelihood of identifying a channel as a ‘badchannel’ can be reduced when a morphological change is due to distancebetween respective nodes instead of a spatial non-correlation betweenthe respective input signals of such nodes.

An identification of the set of nodes and neighboring nodes that exceedthe maximum node distance can be provided as an input to a spatialcorrelation calculator 70 and an amplitude analyzer 72. The spatialcorrelation calculator 70 can be programmed to calculate correlationcoefficients between the input signals for each node not alreadyexcluded and its local neighboring nodes. The spatial correlationcalculator 70 thus computes correlation coefficients from a crosscorrelation between a given central node and its local neighboringnodes, as constrained by the maximum node distance. The correlationcoefficients between a central node and its neighboring nodes can becombined and compared relative to a correlation threshold (e.g.,correlation cutoff value) to determine whether the signals are spatiallynon-correlated or uncorrelated. For example, the spatial correlationcalculator 70 can be configured to compute a cross correlation betweenthe central node and each of its neighbors that yields a coefficientvalue, and a mean correlation value can be computed for each node suchas to provide a single correlation value for each node. The minimum meancorrelation node can be removed from further analysis, including that tobe performed by the amplitude analyzer 72 and following correlationanalysis. The spatial correlation calculator 70 thus compares thecorrelation coefficients relative to the correlation threshold (e.g., acorrelation cutoff value) and recalculates mean correlation values. Thespatial correlation calculator 70 can repeat this process can continueuntil the minimum mean correlation value exceeds the correlation cut offvalue. If any channel had only one remaining neighbor for comparison, itcan be not considered to not be a low integrity channel by the spatialcorrelation calculator 70.

The amplitude analyzer 72 is programmed to identify a proper subset ofchannels having a peak-to-peak amplitude greater than a statisticallysignificant portion of the nodes. For example, the amplitude analyzer 72can perform a histogram analysis of the peak-to-peak amplitude to detectoutliers among each of the remaining channels (e.g., channels notalready identified as bad channels). Those channels in the input dataset provided to the amplitude analyzer that exceed the high amplitudethreshold can be provided to a spatial correlation calculator 74. Thehigh amplitude threshold can be determined from analysis of all thesignal channels, such as based on an amount of variation (e.g., apercentage or a multiple of a standard deviation) greater than meanamplitude of the signals.

The spatial correlation calculator 74 can perform the same correlationas the spatial correlation calculation 70 or it can be different. Forexample, the spatial correlation calculator 74 can compute correlationcoefficients based on performing a cross correlation between the signalsfor each respective node and its local set of neighboring nodes. Asmentioned above, nodes exceeding the maximum node distance are notincluded in this analysis. Additionally, low amplitude channels and highamplitude channels as well as disconnected and channels otherwiseconstrained are also not included in the analysis performed by thespatial correlation calculator 74. As an example, the spatialcorrelation calculator 74 can require a larger amount of correlationthan that required by the analysis implemented by the spatialcorrelation calculator 70 (e.g., the correlation threshold of calculator70). That is the cross correlation performed by the spatial correlationcalculator 74 can employ a more strict correlation threshold than thatemployed by the spatial correlation calculator 70.

A channel aggregator 76 can be configured to combine the list of badchannels detected by the analyzer components of the channel integritydetection system 50, such as including the channel constraint function54, the disconnected channel detector 58, the low amplitude detectionfunction 64, the high amplitude detection function 66, the spatialcorrelation calculator 70 and the spatial correlation calculator 74. Thechannel aggregator 76 in turn can provide a channel integrity list thatcan be utilized to exclude such channels from subsequent analysis. Inother examples, the channel integrity list 78 represent good channels onwhich subsequent analysis is to be performed. In yet another example,the channel integrity list could provide an indication of both goodchannels and bad channels. In still another example, a channel integritylist could provide a quantified value representing a channel integrityfor each of the respective nodes based upon the analysis performed bythe channel integrity detection system 50. Regardless of the contentsand type of information in the channel integrity list, the informationcan be stored in memory in conjunction with the body surface electricaldata 52 for further processing and analysis.

By way of further example, other inputs to and the channel integritydetection system 50 can include variables demonstrated in the followingtable. As disclosed herein some of the variables can be set to defaultvalues or be user programmable. The outputs from the integrity detectionsystem 50 can include variables representing a bad (and/or good) channellist. A list of saturated or disconnected channels can also be provided.

Variable Name Description triangles triangular mesh node connection list(part of the geometry data 56) vertices x, y, z coordinates of each nodepoint (part of or determined from geometry data 56) dataOrig channelinput data (electrical data 52) ccCutOff Correlation coefficient maximumvalue for bad channels (used by correlation calculator 70)ampCutoffSDMultiplier Amplitude standard deviation multiplier (used byamplitude analyzer 72) maxNodeDistValue Maximum node distance (used bynode distance analyzer 68) badChannelZero Previously detected badchannels - saturated (provided by channel constraint 54) channelIndiceschannel indices which references non missing channelsmaxNodeDistMultiplier Maximum node distance standard deviationmultiplier (used by amplitude analyzer 72) ccCutoffAmplitude Amplitudecorrelation coefficient maximum value for bad channels (used bycorrelation calculator 74)

FIG. 3 depicts an example of a system 100 that can be utilized foracquiring electrical activity sensed from a patient 108 and foranalyzing the sensed electrical activity. In some examples, the sensedelectrical activity can be used to generate one or more graphicalrepresentations (e.g., graphical maps of electroanatomic activity) basedon the sensed electrical activity, such as for a region of patientanatomy. The system 100 can include an analysis system 102 that employsa channel integrity detection 104 as disclosed herein.

The analysis system 102 can be implemented as including a computer, suchas a laptop computer, a desktop computer, a server, a tablet computer, aworkstation or the like. The analysis system 102 can include memory 106for storing data and machine-readable instructions. The memory 106 canbe implemented, for example, as a non-transitory computer storagemedium, such as volatile memory (e.g., random access memory),non-volatile memory (e.g., a hard disk drive, a solid-state drive, flashmemory or the like) or a combination thereof.

The analysis system 102 can also include a processing unit 108 to accessthe memory 106 and execute the machine-readable instructions stored inthe memory. The processing unit 108 could be implemented, for example,as one or more processor cores. In the present examples, although thecomponents of the analysis system 102 are illustrated as beingimplemented on the same system, in other examples, the differentcomponents could be distributed across different systems andcommunicate, for example, over a network.

The system 100 can include a measurement system 110 to acquireelectrophysiology information for a patient 112. In the example of FIG.3, a sensor array 114 includes one or more electrodes that can beutilized for recording patient electrical activity. As one example, thesensor array 114 can correspond to an arrangement of body surfaceelectrodes that are distributed over and around the patient's thorax formeasuring electrical activity associated with the patient's heart (e.g.,as part of an ECM procedure). In some examples, there can be about 200or more sensors (e.g., about 252 sensors) in the array 114, each sensorcorresponding to a node that defines a respective channel. An example ofa non-invasive sensor array that can be used is shown and described inInternational application No. PCT/US2009/063803, which was filed 10 Nov.2009, and is incorporated herein by reference. This non-invasive sensorarray corresponds to one example of a full complement of sensors thatcan include one or more sensing zones. As another example, the sensorarray 108 can include an application-specific arrangement of electrodescorresponding to a single sensing zone or multiple discrete sensingzones, such as disclosed in International application No.PCT/US2012/059957, which was filed 12 Oct. 2012, and is incorporatedherein by reference. Additionally or alternatively, the sensor array 114can include invasive sensors that can be inserted into the patient'sbody.

The measurement system 110 receives sensed electrical signals from thecorresponding sensor array 108. The measurement system 110 can includeappropriate controls and signal processing circuitry (e.g., filters andsafety circuitry) for providing corresponding electrical measurementdata 118 that describes electrical activity for each of a plurality ofinput channels detected by the sensors in the sensor array 114.

The measurement data 118 can be stored in the memory 106 as analog ordigital information. Appropriate time stamps and channel identifiers canbe utilized for indexing the respective measurement data 118 tofacilitate the evaluation and analysis thereof. As an example, each ofthe sensors in the sensor array 114 can simultaneously sense bodysurface electrical activity and provide corresponding measurement data118 for one or more user selected time intervals.

The analysis system 102 is configured to process the electricalmeasurement data 118 and to generate one or more outputs. The output canbe stored in the memory 106 and provided to a display 120 or other typeof output device. As disclosed herein, the type of output andinformation presented can vary depending on, for example, applicationrequirements of the user.

As mentioned, the analysis system 102 is programmed to employ channelintegrity detection methods 104 to improve the accuracy in processingand analysis performed by the analysis system. The channel integritydetection 104 can, for example, be implemented to perform anycombination of the channel integrity detection functions and methodsdisclosed herein (see, e.g., FIGS. 1 and 2 and the correspondingdescription). The channel integrity detection 104 thus can compute anindication of which input channels are bad (or good) based on signalprocessing on the measurement data 118. The resulting channel integritydata provided by the detection methods 104 can be stored in the memory106, such as in conjunction with the measurement data 118. In this way,bad channels can be removed automatically or selectively for furtherprocessing and analysis.

In some examples, the channel integrity detection 104 can interface witha graphical user interface (GUI) 122 stored as executable instructionsin the memory 106. The GUI 122 thus can provide an interactive userinterface, such that the thresholds and related parameters utilized bythe channel integrity detection 104 can be set in response to a userinput 124. The GUI 122 can provide data that can be rendered asinteractive graphics on the display 120. For example, the GUI 122 cangenerate an interactive graphical representation that differentiatesbetween good and bad channels (e.g., a graphical representation of thesensor array 114 differentiating graphically or otherwise between badand good channels).

In the example of FIG. 3, the GUI includes a parameter selector 126 thatcan be employed to program channel integrity parameters (e.g.,thresholds and constraints) implemented by the channel integritydetection 104. In some examples, default values can be utilized unlessmodified in response to a user input, such as disclosed herein.

The GUI 122 can also include a channel selector 128 programmed to selectand deselect channels in response to a user input. The channel selector128 can be employed to manually include or exclude selected channels.For instance, the GUI 122 can indicate (e.g., by graphical and/ortextual indicators) on the display 120 which channels are missingchannels to be excluded, a suggested set of channels that are to beexcluded but can be editable via the GUI, and a set of channelsconsidered to be high integrity (e.g., good) channels and are alsoeditable via the GUI. A user can thus employ the channel selector 128 ofthe GUI 122 to include a bad channel that has been identified forremoval or exclude a good channel that is identified for inclusion.

As a further example, the analysis system 102 can include a mappingsystem 130 that is programmed to generate electroanatomical map based onthe measurement data 118, namely based on the measurement data for thechannels determined to have a sufficient integrity (i.e., excluding badchannels). The mapping system 130 can include a map generator 132 thatis programmed to generate map data representing a graphical (e.g., anelectrical or electroanatomic map) based on the measurement data 118.The map generator 132 can generate the map data to visualize such mapvia the display 120 spatially superimposed on a graphical representationof an anatomical structure (e.g., the heart).

In some examples, the mapping system 130 includes a reconstructioncomponent 134 programmed to reconstruct heart electrical activity bycombining the measurement data 118 with geometry data 136 through aninverse calculation. The inverse calculation employs a transformationmatrix and to reconstructs the electrical activity sensed by the sensorarray 114 on the patient's body onto an anatomic envelope, such as anepicardial surface, an endocardial surface or other envelope. Examplesof inverse algorithms that can be implemented by the reconstructioncomponent 134 are disclosed in U.S. Pat. Nos. 7,983,743 and 6,772,004.

The reconstruction component 134, for example, computes coefficients fora transfer matrix to determine heart electrical activity on a cardiacenvelope based on the body surface electrical activity represented bythe electrical measurement data 118. Since the reconstruction onto theenvelope can be sensitive to ingress and other noise on the respectiveinput channels, the channel integrity detection 104 helps to remove datafor channels that would likely adversely affect the process.Additionally, the reconstruction component 134 can utilize interpolatedmeasurement data computed for the identified bad channels. Suchinterpolation for a given channel can be calculated based on signalvalues determined from its neighboring nodes, for example. The possibleeffect of such interpolation on the resolution provided in a graphicalelectroanatomic map can vary depending on the quantity and spatialdistribution of bad channels, as disclosed herein.

The map generator 132 can employ the reconstructed electrical datacomputed via the inverse method to produce corresponding map ofelectrical activity. The map can represent electrical activity of thepatient's heart on the display 120, such as corresponding to a map ofreconstructed electrograms (e.g., a potential map). Alternatively oradditionally, an analysis system 102 can compute other electricalcharacteristics from the reconstructed electrograms, such as anactivation map, a repolarization map, a propagation map or otherelectrical characteristic that can be computed from the measurementdata. The type of map can be set in response to the user input 124 viathe GUI 122.

By way of further example, the patient geometry data 136 can be acquiredusing nearly any imaging modality (e.g., x-ray, computed tomography,magnetic resonance imaging, ultrasound or the like) based on which acorresponding representation can be constructed, such as describedherein. Such imaging may be performed concurrently with recording theelectrical activity that is utilized to generate the measurement data118 or the imaging can be performed separately. As another example, thegeometry data 136 can correspond to a mathematical model of a torso thathas been constructed based on image data for the patient's organ. Ageneric model can also be utilized to provide the geometry data 136. Thegeneric model further may be customized (e.g., deformed) for a givenpatient, such as based on patient characteristics include size imagedata, health conditions or the like. Appropriate anatomical or otherlandmarks, including locations for the electrodes in the sensor array108 can also be represented in the geometry data 116, such as byperforming segmentation of the imaging data. The identification of suchlandmarks can be done manually (e.g., by a person via image editingsoftware) or automatically (e.g., via image processing techniques).

The analysis system 102 can also include a resolution analysis function138 to determine the impact on resolution of analysis performed by themapping system 130 based on the identified bad channels. As an example,the resolution analysis function 138 can include a resolution calculator140 programmed to compute resolution for data that is reconstructed ontoa prescribed surface (e.g., by the reconstruction component). Asmentioned, the surface can include a surface envelope such as caninclude an anatomical surface, a surface of a model or a combination ofa model and anatomical structure onto which electrical data is to bereconstructed, as represented by the geometry data 136. In someexamples, the surface can include an epicardial surface or anendocardial surface of a patient's heart, and further may include anentire surface or a selected region of interest.

A resolution evaluator 142 can analyze the computed resolution over thesurface, such as by comparing the computed resolution relative to athreshold. The threshold can be utilized to determine an area of lowresolution that would be adversely affected by the identified badchannels. The area of low resolution, for example, can be provided tothe map generator 132 and, in turn, be utilized to construct a graphicalmap that can be graphically presented to a user on the display 120. Theuser further can be provided an opportunity to select to continue ormake other adjustments to the sensor array 114 in an effort to improvethe channel integrity. In other examples, a user can select to proceedwith analysis with the understanding that certain areas of thereconstructed data may occupy areas of low resolution and thus couldcontain associated inaccuracies. Such inaccuracies, however, may beinsignificant when a desired region of interest resides outside the areaof low resolution.

FIG. 4 depicts an example of a GUI 200 that includes a first displayportion 202 that includes a graphical depiction of a sensor arrayillustrates sensing nodes. Another portion of the GUI 200 includes adisplay portion 204 representing a set of electrical signals 206. TheGUI 200 can correspond to the GUI 122 of FIG. 3, for example. Theelectrical signals 206 demonstrated in FIG. 4 include signals for aselected set of channels 208 identified as channels 29, 54, 55 and 59 ofthe set of channels. The peak-to-peak amplitude of the channel 54 isapproximately 13 mV. The peak-to-peak amplitude of the third channel 55is approximately 30 mV. The first channel has an approximatepeak-to-peak amplitude of about 30 mV. Each of the channels 54, 55 and59 are examples of high amplitude channels that would be detected by thehigh amplitude detection function of the channel integrity detectionmethod as disclosed herein.

FIG. 5 depicts an example of the GUI 200 from FIG. 4 demonstratingsignals 210 for a different selected set of channels 43, 44, 45, 48 and49, demonstrated at 212. Based on the spatial measurement functionsdisclosed herein, it can be determined that each of the channels 43, 44,45, 48, and 49 exhibits similar morphology, and thus would be spatiallywell correlated. However, the signal for channel 44 has a morphology notsimilar to its respective neighboring channels, and thus can be computedby the channel integrity detection method as a low integrity or badchannel.

As an example, body surface electrophysiological channels are relatedspatially by the connections formed between each sensing node and itscorresponding surrounding nodes. As shown in the example of FIGS. 6 and7, the spatial relationship between nodes can be represented by atriangular mesh.

As a further example, geometry data (e.g., data 56 of FIG. 2 and data136 of FIG. 3) for a segmented image set for a patient while a sensorarray of electrodes is positioned on the patient body can provide datarepresenting each node's spatial location (e.g., an x, y, z coordinateposition). The geometry data can also provide a corresponding triangularmesh connection (e.g., node number triplets for the formation of eachmesh triangle) for each node. For example, in FIG. 7, the nodes 220,222, and 224 (highlighted) were connected by a correspondingtriangulation triplet. The triangular connections across the bodysurface form a triangular mesh which can be used to provide the bodysurface information for subsequent processing, such as for inverseproblem calculations, as disclosed herein.

Each node point on the torso thus is connected by the triangular mesh toone or more neighboring node points. These surrounding nodes areconsidered the node's “neighbors”. An example center node 230 and itslocal neighboring nodes 232, 234, 236, 238 and 240 are shown in FIGS. 8Aand 8B. In one example of comparative calculations (e.g., by thesimilarity measurement 16, amplitude analyzer 18 and similaritymeasurement 20 of FIG. 1), the center node (e.g., node of interest) 230is only compared relative to its adjacent neighboring nodes. In additionto the calculating the neighboring nodes, the node distances betweeneach node and its specific neighbors are calculated (e.g., by the nodedistance function 22 of FIG. 1 or node distance analyzer 68 of FIG. 2).As disclosed herein, the node distances can be used to discriminate poornode comparisons based on distance. While this example includes only animmediately adjacent set of neighboring nodes 232, 234, 236, 238 and 240as neighbors (e.g., which form a neighborhood of nodes), other degreesof proximity can be utilized in other examples. Additionally, thedistance between each center node and its neighboring nodes can beutilized to provide a weighting applied to each correlation between theneighboring nodes. As a result, a more accurate correlation that variesas a function of distance can be utilized in the correlation betweenneighboring channels.

FIGS. 9 and 10 demonstrate examples of a GUI 300, such as can correspondto the GUI 122 of FIG. 3. In the example of FIG. 9, the GUI 300 includesa plurality of display areas, at least some or all of which can includeinteractive GUI elements that can activate functions or methods inresponse to a user input. For example, an interactive electrode displayarea 302 includes a graphical representation of sensing nodes (e.g.,electrodes of a sensor array), such as can correspond to electrodesdistributed on a patient's body as disclosed herein. A scale is providedto inform the user of different levels of channel integrity, such as caninclude ‘Good’ channels, bad channels, bad but editable channels andmissing channels. For example, the scale can utilize different colors,graphical indicia, text or any combination thereof to differentiatechannel integrity that has been determined for each such channel, suchas shown in FIG. 9. In this example one of the nodes 304 has beenselected and its corresponding signal is presented in display area 306.Any number of one or more nodes can be selected to provide its signal inthe display area 306. An adjacent display area 308 includes waveformsfor each of the electrodes that have not been removed by the channelintegrity detection or that has been removed (e.g., an editable channel)but has been reactivated by the user.

Also demonstrated in FIG. 9 is a graphical map 310. In this example, thegraphical map 310 includes a graphical representation (e.g., via colorcoding) of one or more areas of low resolution, demonstrated at 312. Thearea of low resolution, for example, can be determined by a resolutionanalysis method (e.g., resolution analysis method 138 of FIG. 3) inconjunction with reconstruction of the sensed signals to a surface(e.g., the cardiac surface). Thus, the graphical map 310 can display theeffect that the identified bad channels will have on the overallresolution of inverse calculations. In this example, the area of lowresolution is the result of several bad channels, highlighted at 314,near the low edge of each panel of the array of electrodes. A user thushas an opportunity to cancel the process and adjust the sensingelectrodes or the user can select to continue (e.g., via GUI elements316) the process.

The GUI 300 also includes GUI elements 318 that can be utilized toselect what type of map will be generated (e.g., by map generator 132 ofFIG. 3) and presented in the map 310 in response to a user input.Examples of maps that can be created can include a potential map, anactivation map, a voltage map, a slew rate map and a propagation map.Other maps could also be generated.

FIG. 10 depicts another example of the GUI 300 in which the samereference characters refer to the same parts introduced with respect toFIG. 9. In the example of FIG. 10, a given node 320 has been selected inresponse to a user input. The corresponding waveform is presented in thedisplay area 306. The resulting mapped electrode followingreconstruction for each of the good electrodes is demonstrated indisplay area 308. Additionally, since the selected node in this examplehas been determined (e.g., by channel integrity detection method 104 ofFIG. 3) to be a good channel, its reconstructed waveform is highlighted(e.g., graphically differentiated) from the other reconstructedwaveforms in the display area 308, as shown at 322. Similar to FIG. 9,the map 310 includes areas of low resolution 312 resulting from theimpact of channels that have been determined to be bad channels (e.g.,by channel integrity detection method 104 of FIG. 3).

In view of the foregoing, an automatic bad channel detection method hasbeen disclosed to improve accuracy and user experience. The approachdisclosed herein thus can enhance the user interaction and increase theease of beat-by-beat analysis. The bad channel detection methods andsystems can be implemented to identify and remove high amplitude and lowspatially correlated signal channels. The remaining channels can beutilized to reconstruct electrical activity on a surface envelope (e.g.,epicardial or endocardial electro grams) via potential-based inverseelectrocardiography algorithms.

As will be appreciated by those skilled in the art, portions of theinvention may be embodied as a method, data processing system, orcomputer program product. Accordingly, these portions of the presentinvention may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware. Furthermore, portions of the invention may be a computerprogram product on a computer-usable storage medium having computerreadable program code on the medium. Any suitable computer-readablemedium may be utilized including, but not limited to, static and dynamicstorage devices, hard disks, optical storage devices, and magneticstorage devices.

Certain embodiments of the invention are described herein with referenceto flowchart illustrations of methods, systems, and computer programproducts. It will be understood that blocks of the illustrations, andcombinations of blocks in the illustrations, can be implemented bycomputer-executable instructions. These computer-executable instructionsmay be provided to one or more processor of a general purpose computer,special purpose computer, or other programmable data processingapparatus (or a combination of devices and circuits) to produce amachine, such that the instructions, which execute via the processor,implement the functions specified in the block or blocks.

These computer-executable instructions may also be stored incomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory result in an article of manufacture including instructions whichimplement the function specified in the flowchart block or blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

What have been described above are examples. It is, of course, notpossible to describe every conceivable combination of components ormethodologies, but one of ordinary skill in the art will recognize thatmany further combinations and permutations are possible. Accordingly,the disclosure is intended to embrace all such alterations,modifications, and variations that fall within the scope of thisapplication, including the appended claims.

As used herein, the term “includes” means includes but not limited to,the term “including” means including but not limited to. The term “basedon” means based at least in part on. Additionally, where the disclosureor claims recite “a,” “an,” “a first,” or “another” element, or theequivalent thereof, it should be interpreted to include one or more thanone such element, neither requiring nor excluding two or more suchelements.

What is claimed is:
 1. A system comprising: a plurality of electrodes tomeasure electrical signals corresponding to input channel data from apatient, each of the plurality of electrodes corresponding to a nodethat defines a respective input channel; and a processor to accessmemory comprising instructions executable by the processor, theinstructions comprising: a first spatial similarity measurement functionto compute a measure of similarity between the input channel data foreach node and a set of neighboring nodes to identify a spatialcorrelated set of channels having an integrity that is considered one ofbad or good; an amplitude analyzer programmed to determine a subset ofchannels meeting amplitude criteria; a second spatial similaritymeasurement function to compute, for each channel in the subset ofchannels meeting the amplitude criteria, a measure of similarity betweenthe input channel data for each node and a set of neighboring nodes toidentify an amplitude correlated set of channels having an integritythat is considered one of bad or good; and a combiner programmed togenerate output data representing the integrity of the plurality ofinput channels based on the spatial correlated set of channels and theamplitude correlated set of channels; and a display device to provide agraphical representation of the output data that includes an indicationof the integrity of the plurality of input channels, wherein the one ormore channels with low integrity are selectively included or excludedfrom further processing according to a user input.
 2. The system ofclaim 1, wherein the amplitude analyzer is further programmed to comparean amplitude value for each node relative to amplitude values for atleast a substantial portion of the other nodes to determine temporarybad channels, which defines the subset of channels meeting the amplitudecriteria.
 3. The system of claim 2, wherein the second spatialsimilarity measurement function comprises a correlation calculatorprogrammed to compute a cross correlation between the input channel datafor each node, corresponding to the temporary bad channels, and the setof neighboring nodes.
 4. The system of claim 2, wherein the instructionsfurther comprise an amplitude calculator programmed to compute theamplitude values for each of the plurality of nodes based on the inputchannel data for each respective node, wherein the second spatialsimilarity measurement function comprises a correlation calculatorprogrammed to compute a correlation coefficient value between thecomputed amplitude value of each of the temporary bad channels and itslocal neighboring nodes, the amplitude correlated set of channels beingdetermined based on a comparison of the correlation coefficient valuecomputed for each node relative to a threshold value.
 5. The system ofclaim 1, wherein the first spatial similarity measurement functioncomprises a correlation calculator programmed to compute correlationcoefficient values from a cross correlation computed between each of theplurality of nodes and its local neighboring nodes, the spatialcorrelated set of channels being determined based on a comparison of thecorrelation coefficient value for each node relative to a thresholdvalue.
 6. The system of claim 1, wherein the instructions furthercomprise a node distance analyzer programmed to compute distance betweennodes based on locations of nodes determined from geometry data computedfrom image data that represents locations for the plurality of nodes,the geometry data being computed from image data, each set ofneighboring nodes being determined based on the distance between nodes.7. The system of claim 1, wherein the instructions further comprise apreprocessing stage programmed to analyze the input channel data todetect channels corresponding to the plurality of electrodes having anintegrity that is considered one of bad or good, the combiner beingprogrammed to generate output data representing the integrity of theplurality of input channels based on the integrity of the channelsdetected by the preprocessing stage, the spatial correlated set ofchannels and the amplitude correlated set of channels.
 8. The system ofclaim 1, wherein the instructions further are programmed toautomatically remove from the further processing each of the channelsidentified as having low channel integrity, the user input is tooverride or accept the automatic removal of channels from the furtherprocessing.
 9. The system of claim 7, wherein the preprocessing stagefurther comprises a low amplitude detector programmed to identify eachchannel having an amplitude value that resides below a low amplitudethreshold, the output data including channels identified by the lowamplitude detector.
 10. The system of claim 7, wherein the preprocessingstage further comprises a high amplitude detector programmed to identifyeach channel having an amplitude value that resides above a highamplitude threshold, the output data including channels identified bythe high amplitude detector.
 11. The system of claim 1, wherein theinstructions further comprise: a resolution calculator programmed tocompute coefficients of a transformation matrix for at least asubstantial portion of the plurality of input channels based on the datarepresenting the integrity of the plurality of input channels; and anevaluator programmed to identify a low resolution spatial region basedon an evaluation of the coefficients of the transformation matrix, amapping generator programmed to generate a graphical map depicting thelow resolution spatial region.
 12. The system of claim 1, wherein theinstructions further comprise a mapping system programmed to generate areconstructed set of signals on an envelope based on the input channeldata and the output data, such that an interpolated value is used foreach bad channel that is excluded.
 13. The system of claim 1, whereinthe plurality of electrodes are one of positioned across a body surfaceof the patient or a surface of an internal anatomical structure of thepatient.
 14. The system of claim 13, wherein the internal anatomicalstructure is a heart.
 15. A system comprising: a sensor array comprisinga plurality of electrodes to measure electrical activity across at leastone of a body surface of a patient and a surface of an internalanatomical structure of the patient, each of the plurality of electrodescorresponding to a node that defines a respective input channel; ananalysis system to: determine an amplitude for each respective inputchannel; compute a measure of similarity between the input channel ofeach node and the input channel of its neighboring nodes; compare theamplitude for each node relative to other nodes to determine temporarybad channels; for each of the temporary bad channels, compute a measureof similarity between the input channel of each node and the inputchannel of its neighboring nodes; identify channel integrity for eachrespective input channel based on the computed measures of similarity;and store data associated with the identified channel integrity for eachrespective input channel in a memory; and a display to provide agraphical representation of the data associated with the identifiedchannel integrity for each respective input channel, wherein thegraphical representation illustrates one or more input channels with lowintegrity, wherein the one or more channels with low integrity areselectively included or excluded from further processing according to auser input.
 16. The system of claim 15, wherein the analysis system isfurther to: compute a resolution of reconstructed signals based on aplurality of input channels; identify at least one region of lowresolution resulting based on the computed resolution; and generate adisplay of a graphical map representing the region of low resolution.17. The system of claim 15, wherein the internal anatomical structure isa heart.
 18. The system of claim 15, wherein the analysis system isfurther to automatically remove from the further processing each of thechannels identified as having low channel integrity, the user input isto override or accept the automatic removal of channels from the furtherprocessing.
 19. The system of claim 16, wherein the analysis system isfurther to generate the reconstructed signals using an inverse methodbased on the plurality of input channels, in which the identified badchannels are excluded from the generating and an interpolated channelvalue is used in place each identified bad channel.
 20. The system ofclaim 15, wherein the analysis system is further programmed to determinea disconnected condition of a sensor for a given input channel toidentify at least one bad channel.
 21. The system of claim 15, whereinthe measure of similarity computed for each of the temporary badchannels further comprises: computing a correlation coefficient valuebetween the computed amplitude value of each of the temporary badchannels and its neighboring nodes; and comparing the correlationcoefficient value computed for each node relative to a threshold value.22. The system of claim 21, wherein the threshold value is a firstthreshold value and wherein the measure of similarity computed for eachof the input channels further comprises: computing a correlationcoefficient value between the computed amplitude value of each of thetemporary bad channels and its neighboring nodes; and comparing thecorrelation coefficient value computed for each node relative to asecond threshold value.